De Nunzio Cosimo, Autorino Riccardo, Bachmann Alexander, Briganti Alberto, Carter Simon, Chun Felix, Novara Giacomo, Sosnowski Roman, Thiruchelvam Nickesh, Tubaro Andrea, Ahyai Sascha
Department of Urology, Sant' Andrea Hospital "La Sapienza,", Rome, Italy.
Department of Urology, Urology Clinic, Second University of Naples, Naples, Italy.
Neurourol Urodyn. 2016 Feb;35(2):235-40. doi: 10.1002/nau.22705. Epub 2014 Dec 18.
To develop a nomogram predicting benign prostatic obstruction (BPO).
We included in this study 600 men with lower urinary tract symptoms (LUTS) and benign prostatic enlargement (BPE) who underwent standardized pressure flow studies (PFS) between 1996 and 2000. Complete clinical and urodynamic data were available for all patients. Variables assessed in univariate and multivariate logistic regression models consisted of IPSS, PSA, prostate size, maximal urinary flow rate (Qmax) at free flow, residual urine (RU), and bladder wall thickness (BWT). These were used to predict significant BPO (defined as a Schäfer grade ≥ 3 in PFS).
A preliminary multivariate model, including IPSS, Qmax at free flow and RU, suggested that only Qmax at free flow was a statistically significant predictor of BPO (P = 0.00) with a predictive accuracy (PA) of 82%. Further development of the multivariate model showed how the inclusion of BWT did not increase PA. Only transitional zone volume (TZV) proved to be an additional statistically significant predictor for BPO (P = 0.00). The combination of Qmax at free flow and TZV demonstrated a PA of 83.2% and were included in the final nomogram format.
We developed a clinical nomogram, which is both accurate and well calibrated, which can be helpful in the management of patients with LUTS and BPE. External validation is warranted to confirm our findings.
开发一种预测良性前列腺梗阻(BPO)的列线图。
本研究纳入了600例有下尿路症状(LUTS)且前列腺良性增生(BPE)的男性患者,他们在1996年至2000年间接受了标准化压力流率研究(PFS)。所有患者均有完整的临床和尿动力学数据。在单因素和多因素逻辑回归模型中评估的变量包括国际前列腺症状评分(IPSS)、前列腺特异性抗原(PSA)、前列腺大小、自由尿流率(Qmax)、残余尿量(RU)和膀胱壁厚度(BWT)。这些变量用于预测显著BPO(PFS中定义为Schäfer分级≥3级)。
一个初步的多因素模型,包括IPSS、自由尿流率Qmax和RU,表明只有自由尿流率Qmax是BPO的统计学显著预测因子(P = 0.00),预测准确率(PA)为82%。多因素模型的进一步发展表明,纳入BWT并没有提高PA。只有移行区体积(TZV)被证明是BPO的另一个统计学显著预测因子(P = 0.00)。自由尿流率Qmax和TZV的组合显示PA为83.2%,并被纳入最终的列线图形式。
我们开发了一种临床列线图,其既准确又校准良好,有助于LUTS和BPE患者的管理。需要进行外部验证以证实我们的发现。